Traffic congestion is a common problem for big cities all over the world. To manage traffic congestion, many big cities have built information technology (IT) systems to interpret traffic situations based on average travel speed for each road section. Average travel speed is an intuitive way to illustrate the reduction of mobility experienced during congestion, and is widely used in interpreting meso-level and macro-level traffic congestion.
Different traffic sensor data may be used in speed calculation. One type of data is the floating car data (FCD), which typically includes global positioning system (GPS) data from onboard devices in taxicabs. FCD is widely used because such data collection is effective and economical, and can be easily integrated with a geographical information system and summarized at any level.
Taxicabs are typically not equally distributed in a road network, which means there are different sample sizes for calculating average travel speed for different road sections. For extreme cases, a recognized traffic congestion point may be totally wrong as there may be only a single abnormal taxicab available for speed calculation in a certain road section. If random errors are not taken into account in speed calculation, a stable and reliable outcome cannot be determined.